Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Detect project maturity stage (new/early/mid/mature) from file structure and route to appropriate onboarding workflow
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Detect the maturity stage of a software project by analyzing its file structure, configuration files, documentation, and test coverage. Route to the appropriate onboarding or initialization workflow based on the detected stage.
Skill({ skill: 'project-stage-detection' });
Invoke when:
| Stage | Description | Indicators |
|---|---|---|
new | Freshly initialized, no meaningful content | Only boilerplate files, no src/ |
early | Has core structure but missing key components | src/ exists, no tests or CI |
mid | Functional codebase, gaps in quality infrastructure | Tests exist, no CI/CD or sparse docs |
mature | Full-featured with CI/CD, tests, docs, linting | All quality gates present |
Command:
node .claude/skills/project-stage-detection/scripts/main.cjs --dir <project_root>
Expected output: JSON with stage, confidence, indicators, and recommendations.
Verify: Exit code 0 and valid JSON with a stage field.
The script checks for these indicators, scoring each:
| Indicator | Weight | Checked Path(s) |
|---|---|---|
| Source directory exists | HIGH | src/, lib/, app/ |
| Tests exist | HIGH | tests/, test/, spec/, __tests__/ |
| CI/CD pipeline configured | HIGH | .github/workflows/, .gitlab-ci.yml |
| Package.json / pyproject.toml | MED | package.json, pyproject.toml |
| README exists and non-trivial | MED | README.md (>500 bytes) |
| Linting configured | MED | .eslintrc*, .ruff.toml, pylintrc |
| Documentation directory | LOW | docs/, .claude/docs/ |
| Changelog present | LOW | CHANGELOG.md, CHANGELOG.rst |
| Dependencies locked | LOW | package-lock.json, pnpm-lock.yaml |
| Score Range | Stage |
|---|---|
| 0–2 | new |
| 3–5 | early |
| 6–7 | mid |
| 8–9 | mature |
Command to verify score:
node .claude/skills/project-stage-detection/scripts/main.cjs --dir . --json | node -e "const d=require('fs').readFileSync('/dev/stdin','utf8');const r=JSON.parse(d);console.log('Stage:',r.stage,'Score:',r.score,'Confidence:',r.confidence)"
Based on the detected stage:
| Stage | Recommended Action |
|---|---|
new | Invoke project-onboarding skill to bootstrap |
early | Invoke gap-detection to find missing infrastructure |
mid | Invoke gap-detection then proactive-audit |
mature | Invoke gap-detection as health check only |
Routing command:
// After detection
if (result.stage === 'new') {
Skill({ skill: 'project-onboarding' });
} else {
Skill({ skill: 'gap-detection' });
}
Output location: .claude/context/reports/backend/project-stage-report-YYYY-MM-DD.md
Report format:
# Project Stage Detection Report
**Date:** {{date}}
**Project:** {{project_root}}
**Detected Stage:** {{stage}}
**Confidence:** {{confidence}}%
## Evidence
{{indicators_table}}
## Recommendations
{{recommendations_list}}
See schemas/output.schema.json for the full structured output contract.
Input validated against schemas/input.schema.json before execution.
Output contract defined in schemas/output.schema.json.
planner — uses on task start to calibrate workflow complexityarchitect — uses for brownfield assessment before redesigndeveloper — uses to determine onboarding steps for new reposmaster-orchestrator — uses to route to correct initialization workflowBefore starting:
Read .claude/context/memory/learnings.md for any previously detected stage for this project. If the stage was recently detected (within 7 days), skip re-detection and use cached result.
After completing:
Append to .claude/context/memory/learnings.md:
## Project Stage Detection — {{date}}
- Project: {{project_root}}
- Stage: {{stage}} (confidence: {{confidence}}%)
- Key gaps: {{gap_summary}}
- Recommended next action: {{next_action}}
mature for a project with no tests — tests are a required mature indicator..claude/ subdirectories — scan the project root only.existsSync checks.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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